jonaspauleta

Native Postgres full-text search + pg_trgm engine for Laravel Scout. Drop-in replacement for Meilisearch/Algolia/Typesense when you already run Postgres.

12
0
100% credibility
Found Apr 26, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
PHP
AI Summary

A Laravel Scout driver that implements full-text search directly in PostgreSQL using generated columns and GIN indexes for efficient querying without external search services.

How It Works

1
🔍 Discover smart search

You hear about a handy tool that adds powerful search to your app using your existing database, no extra services needed.

2
📦 Add to your project

You easily include this search helper into your app with a quick install step.

3
⚙️ Prepare your data

You update your app's data setup once to enable automatic search preparation on saves and changes.

4
Mark searchable items

You simply point out which fields like titles, descriptions, or names should be searchable, giving them importance levels.

5
🧪 Run your first search

You try searching for words, phrases, or even with typos in your app's data.

🚀 Enjoy fast searches

Your app now delivers quick, accurate results with prefix matching and fuzzy tolerance, keeping everything in sync effortlessly.

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Star Growth

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AI-Generated Review

What is scout-postgres?

Scout-postgres is a PHP package that adds native PostgreSQL full-text search and trigram similarity as a drop-in engine for Laravel Scout. It lets you search models with `Model::search()` without spinning up Meilisearch, Algolia, or Typesense—ideal if you already run Postgres 14+. Setup involves a migration macro for generated search columns, then Scout handles querying with prefix matching, typo tolerance, and accent insensitivity out of the box.

Why is it gaining traction?

It skips the ops headache of extra search services: no queues, no drift, just GIN-indexed columns that update transactionally in your existing Postgres. Benchmarks crush Scout's database driver on multi-token queries and fuzzy matches, hitting sub-10ms p50 on 500k rows. Per-column weights and adaptive strategies tune relevance without custom code.

Who should use this?

Laravel backend devs with Postgres apps needing search on catalogs of hundreds of thousands to low-millions rows, like product listings or content sites. Suits teams avoiding managed search costs or sync complexity, but skip if you need built-in faceting, geo, or per-row multilingual configs.

Verdict

Grab it if you're on Laravel 11+ and Postgres already—saves time and cash for the 80% case. At 12 stars and 1.0% credibility, it's early but stable (v1.0), with strong docs, CI tests, and benchmarks; watch for adoption before production lock-in.

(198 words)

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